{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-10-23T07:20:30.709925Z","iopub.execute_input":"2022-10-23T07:20:30.710369Z","iopub.status.idle":"2022-10-23T07:20:30.721750Z","shell.execute_reply.started":"2022-10-23T07:20:30.710336Z","shell.execute_reply":"2022-10-23T07:20:30.720819Z"},"trusted":true},"execution_count":114,"outputs":[{"name":"stdout","text":"/kaggle/input/good-bad/dataset2022.xlsx\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:20:31.038769Z","iopub.execute_input":"2022-10-23T07:20:31.039141Z","iopub.status.idle":"2022-10-23T07:20:31.047192Z","shell.execute_reply.started":"2022-10-23T07:20:31.039111Z","shell.execute_reply":"2022-10-23T07:20:31.045940Z"},"trusted":true},"execution_count":115,"outputs":[]},{"cell_type":"markdown","source":"## 数据预处理-基本描述 1.2","metadata":{}},{"cell_type":"code","source":"# 加载数据\ndf = pd.read_excel('/kaggle/input/good-bad/dataset2022.xlsx', index_col=[0])\nprint(df.shape)\ndf.head(5)","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:20:32.415691Z","iopub.execute_input":"2022-10-23T07:20:32.416550Z","iopub.status.idle":"2022-10-23T07:20:48.290326Z","shell.execute_reply.started":"2022-10-23T07:20:32.416479Z","shell.execute_reply":"2022-10-23T07:20:48.289109Z"},"trusted":true},"execution_count":116,"outputs":[{"name":"stdout","text":"(20000, 50)\n","output_type":"stream"},{"execution_count":116,"output_type":"execute_result","data":{"text/plain":"          member_id loan_status  loan_amnt    issue_d        term  int_rate  \\\nid                                                                            \n11435650   13367814  Fully Paid      32000 2020-01-14   36 months    0.0967   \n11385707   13327865  Fully Paid       5000 2020-01-14   36 months    0.1922   \n11405625   13337797  Fully Paid      19750 2020-01-14   36 months    0.1398   \n11375637   13307791  Fully Paid       3500 2020-01-14   36 months    0.1353   \n11405664   13337837  Fully Paid       4000 2020-01-14   36 months    0.0662   \n\n          installment grade emp_length home_ownership  ...  num_rev_accts  \\\nid                                                     ...                  \n11435650      1027.60     B    3 years       MORTGAGE  ...             10   \n11385707       183.84     D    2 years           RENT  ...             12   \n11405625       674.82     C   < 1 year           RENT  ...             16   \n11375637       118.83     B  10+ years           RENT  ...             24   \n11405664       122.82     A    2 years            OWN  ...              5   \n\n          num_rev_tl_bal_gt_0  num_sats  num_tl_op_past_12m  pct_tl_nvr_dlq  \\\nid                                                                            \n11435650                    6        11                   2            94.4   \n11385707                    2         7                   5           100.0   \n11405625                    8        19                   2           100.0   \n11375637                    6         9                   0            92.3   \n11405664                    3        19                   1           100.0   \n\n          percent_bc_gt_75  tot_hi_cred_lim  total_bal_ex_mort  \\\nid                                                               \n11435650              14.3           292644              39963   \n11385707              66.7            41740              32010   \n11405625              40.0           143760              70229   \n11375637             100.0             8000               4766   \n11405664               0.0           116674             108141   \n\n          total_bc_limit  total_il_high_credit_limit  \nid                                                    \n11435650           37100                       48756  \n11385707           12500                       27340  \n11405625           99400                       40960  \n11375637            2200                           0  \n11405664            8500                      106274  \n\n[5 rows x 50 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>member_id</th>\n      <th>loan_status</th>\n      <th>loan_amnt</th>\n      <th>issue_d</th>\n      <th>term</th>\n      <th>int_rate</th>\n      <th>installment</th>\n      <th>grade</th>\n      <th>emp_length</th>\n      <th>home_ownership</th>\n      <th>...</th>\n      <th>num_rev_accts</th>\n      <th>num_rev_tl_bal_gt_0</th>\n      <th>num_sats</th>\n      <th>num_tl_op_past_12m</th>\n      <th>pct_tl_nvr_dlq</th>\n      <th>percent_bc_gt_75</th>\n      <th>tot_hi_cred_lim</th>\n      <th>total_bal_ex_mort</th>\n      <th>total_bc_limit</th>\n      <th>total_il_high_credit_limit</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>11435650</th>\n      <td>13367814</td>\n      <td>Fully Paid</td>\n      <td>32000</td>\n      <td>2020-01-14</td>\n      <td>36 months</td>\n      <td>0.0967</td>\n      <td>1027.60</td>\n      <td>B</td>\n      <td>3 years</td>\n      <td>MORTGAGE</td>\n      <td>...</td>\n      <td>10</td>\n      <td>6</td>\n      <td>11</td>\n      <td>2</td>\n      <td>94.4</td>\n      <td>14.3</td>\n      <td>292644</td>\n      <td>39963</td>\n      <td>37100</td>\n      <td>48756</td>\n    </tr>\n    <tr>\n      <th>11385707</th>\n      <td>13327865</td>\n      <td>Fully Paid</td>\n      <td>5000</td>\n      <td>2020-01-14</td>\n      <td>36 months</td>\n      <td>0.1922</td>\n      <td>183.84</td>\n      <td>D</td>\n      <td>2 years</td>\n      <td>RENT</td>\n      <td>...</td>\n      <td>12</td>\n      <td>2</td>\n      <td>7</td>\n      <td>5</td>\n      <td>100.0</td>\n      <td>66.7</td>\n      <td>41740</td>\n      <td>32010</td>\n      <td>12500</td>\n      <td>27340</td>\n    </tr>\n    <tr>\n      <th>11405625</th>\n      <td>13337797</td>\n      <td>Fully Paid</td>\n      <td>19750</td>\n      <td>2020-01-14</td>\n      <td>36 months</td>\n      <td>0.1398</td>\n      <td>674.82</td>\n      <td>C</td>\n      <td>&lt; 1 year</td>\n      <td>RENT</td>\n      <td>...</td>\n      <td>16</td>\n      <td>8</td>\n      <td>19</td>\n      <td>2</td>\n      <td>100.0</td>\n      <td>40.0</td>\n      <td>143760</td>\n      <td>70229</td>\n      <td>99400</td>\n      <td>40960</td>\n    </tr>\n    <tr>\n      <th>11375637</th>\n      <td>13307791</td>\n      <td>Fully Paid</td>\n      <td>3500</td>\n      <td>2020-01-14</td>\n      <td>36 months</td>\n      <td>0.1353</td>\n      <td>118.83</td>\n      <td>B</td>\n      <td>10+ years</td>\n      <td>RENT</td>\n      <td>...</td>\n      <td>24</td>\n      <td>6</td>\n      <td>9</td>\n      <td>0</td>\n      <td>92.3</td>\n      <td>100.0</td>\n      <td>8000</td>\n      <td>4766</td>\n      <td>2200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11405664</th>\n      <td>13337837</td>\n      <td>Fully Paid</td>\n      <td>4000</td>\n      <td>2020-01-14</td>\n      <td>36 months</td>\n      <td>0.0662</td>\n      <td>122.82</td>\n      <td>A</td>\n      <td>2 years</td>\n      <td>OWN</td>\n      <td>...</td>\n      <td>5</td>\n      <td>3</td>\n      <td>19</td>\n      <td>1</td>\n      <td>100.0</td>\n      <td>0.0</td>\n      <td>116674</td>\n      <td>108141</td>\n      <td>8500</td>\n      <td>106274</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 50 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# 查看全部变量缺失值情况\n'''\nemp_length                    1067\nc_open_to_buy                 152\nbc_util                        162\nmo_sin_old_il_acct             561\nmths_since_recent_bc           141\npercent_bc_gt_75               169\n'''\ndf.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:20:48.292182Z","iopub.execute_input":"2022-10-23T07:20:48.292694Z","iopub.status.idle":"2022-10-23T07:20:48.310001Z","shell.execute_reply.started":"2022-10-23T07:20:48.292648Z","shell.execute_reply":"2022-10-23T07:20:48.308470Z"},"trusted":true},"execution_count":117,"outputs":[{"execution_count":117,"output_type":"execute_result","data":{"text/plain":"member_id                        0\nloan_status                      0\nloan_amnt                        0\nissue_d                          0\nterm                             0\nint_rate                         0\ninstallment                      0\ngrade                            0\nemp_length                    1067\nhome_ownership                   0\nFICO                             0\nannual_inc                       0\nloantoincome                     0\ndti                              0\ndelinq_2yrs                      0\ninq_last_6mths                   0\nopen_acc                         0\npub_rec                          0\nrevol_bal                        0\nrevol_util                       5\ntotal_acc                        0\ntot_cur_bal                      0\ntotal_rev_hi_lim                 0\nacc_open_past_24mths             0\navg_cur_bal                      0\nbc_open_to_buy                 152\nbc_util                        162\nmo_sin_old_il_acct             561\nmo_sin_old_rev_tl_op             0\nmo_sin_rcnt_rev_tl_op            0\nmo_sin_rcnt_tl                   0\nmort_acc                         0\nmths_since_recent_bc           141\nnum_accts_ever_120_pd            0\nnum_actv_bc_tl                   0\nnum_actv_rev_tl                  0\nnum_bc_sats                      0\nnum_bc_tl                        0\nnum_il_tl                        0\nnum_op_rev_tl                    0\nnum_rev_accts                    0\nnum_rev_tl_bal_gt_0              0\nnum_sats                         0\nnum_tl_op_past_12m               0\npct_tl_nvr_dlq                   0\npercent_bc_gt_75               169\ntot_hi_cred_lim                  0\ntotal_bal_ex_mort                0\ntotal_bc_limit                   0\ntotal_il_high_credit_limit       0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# 分析emp_length\nprint(df['emp_length'].unique())\ndf['emp_length'].hist(figsize=(12,4))","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:20:48.312051Z","iopub.execute_input":"2022-10-23T07:20:48.312734Z","iopub.status.idle":"2022-10-23T07:20:48.607367Z","shell.execute_reply.started":"2022-10-23T07:20:48.312686Z","shell.execute_reply":"2022-10-23T07:20:48.606437Z"},"trusted":true},"execution_count":118,"outputs":[{"name":"stdout","text":"['3 years' '2 years' '< 1 year' '10+ years' '4 years' '8 years' '7 years'\n nan '5 years' '6 years' '9 years' '1 year']\n","output_type":"stream"},{"execution_count":118,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 864x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# 使用众数填充emp_length(理由众数占据的比例很明显，如果使用均值或者0值填充会比较大改变数据原始分布)\nimport statistics\ndf[\"emp_length\"] = df[\"emp_length\"].replace(np.NaN, statistics.mode(df[\"emp_length\"]))","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:14.125176Z","iopub.execute_input":"2022-10-23T07:21:14.126283Z","iopub.status.idle":"2022-10-23T07:21:14.143557Z","shell.execute_reply.started":"2022-10-23T07:21:14.126228Z","shell.execute_reply":"2022-10-23T07:21:14.142379Z"},"trusted":true},"execution_count":119,"outputs":[]},{"cell_type":"code","source":"#分析 bc_open_to_buy\nprint(df['bc_open_to_buy'].unique())\ndf['bc_open_to_buy'].hist()","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:16.319124Z","iopub.execute_input":"2022-10-23T07:21:16.320031Z","iopub.status.idle":"2022-10-23T07:21:16.559850Z","shell.execute_reply.started":"2022-10-23T07:21:16.319978Z","shell.execute_reply":"2022-10-23T07:21:16.558458Z"},"trusted":true},"execution_count":120,"outputs":[{"name":"stdout","text":"[14883.  5694. 74204. ... 59875. 10476. 20900.]\n","output_type":"stream"},{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# 分析 bc_open_to_buy 使用中位数或者均值填充。因为数据集中分布且为连续变量\n#Mean - 缺失值\ndf['bc_open_to_buy'] = df['bc_open_to_buy'].replace(np.NaN, df['bc_open_to_buy'].mean())","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:17.556606Z","iopub.execute_input":"2022-10-23T07:21:17.557417Z","iopub.status.idle":"2022-10-23T07:21:17.564184Z","shell.execute_reply.started":"2022-10-23T07:21:17.557349Z","shell.execute_reply":"2022-10-23T07:21:17.563267Z"},"trusted":true},"execution_count":121,"outputs":[]},{"cell_type":"code","source":"# 分析 bc_util \nprint(df['bc_util'].unique())\ndf['bc_util'].hist()","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:18.171577Z","iopub.execute_input":"2022-10-23T07:21:18.172208Z","iopub.status.idle":"2022-10-23T07:21:18.433370Z","shell.execute_reply.started":"2022-10-23T07:21:18.172173Z","shell.execute_reply":"2022-10-23T07:21:18.432257Z"},"trusted":true},"execution_count":122,"outputs":[{"name":"stdout","text":"[ 59.9  54.4  25.3 ... 118.  109.8 149.1]\n","output_type":"stream"},{"execution_count":122,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# 填充 bc_util 使用中位数填充，数据为连续变量\ndf['bc_util'] = df['bc_util'].replace(np.NaN, df['bc_util'].median())","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:18.814951Z","iopub.execute_input":"2022-10-23T07:21:18.815379Z","iopub.status.idle":"2022-10-23T07:21:18.822504Z","shell.execute_reply.started":"2022-10-23T07:21:18.815343Z","shell.execute_reply":"2022-10-23T07:21:18.821300Z"},"trusted":true},"execution_count":123,"outputs":[]},{"cell_type":"code","source":"# mo_sin_old_il_acct\ndf['mo_sin_old_il_acct'].hist()\ndf['mo_sin_old_il_acct'] = df['mo_sin_old_il_acct'].replace(np.NaN, df['mo_sin_old_il_acct'].median())","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:19.114224Z","iopub.execute_input":"2022-10-23T07:21:19.114642Z","iopub.status.idle":"2022-10-23T07:21:19.356697Z","shell.execute_reply.started":"2022-10-23T07:21:19.114606Z","shell.execute_reply":"2022-10-23T07:21:19.355375Z"},"trusted":true},"execution_count":124,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"df['mths_since_recent_bc'].hist()\ndf['mths_since_recent_bc'] = df['mths_since_recent_bc'].replace(np.NaN, df['mths_since_recent_bc'].mean())","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:19.442010Z","iopub.execute_input":"2022-10-23T07:21:19.442442Z","iopub.status.idle":"2022-10-23T07:21:19.708936Z","shell.execute_reply.started":"2022-10-23T07:21:19.442392Z","shell.execute_reply":"2022-10-23T07:21:19.707619Z"},"trusted":true},"execution_count":125,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# percent_bc_gt_75 删除行\ndf['percent_bc_gt_75'].hist()\ndf.dropna(inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:20.307711Z","iopub.execute_input":"2022-10-23T07:21:20.308123Z","iopub.status.idle":"2022-10-23T07:21:20.568831Z","shell.execute_reply.started":"2022-10-23T07:21:20.308092Z","shell.execute_reply":"2022-10-23T07:21:20.567525Z"},"trusted":true},"execution_count":126,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# 查看缺失情况\ndf.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:21.513735Z","iopub.execute_input":"2022-10-23T07:21:21.514140Z","iopub.status.idle":"2022-10-23T07:21:21.531825Z","shell.execute_reply.started":"2022-10-23T07:21:21.514107Z","shell.execute_reply":"2022-10-23T07:21:21.530523Z"},"trusted":true},"execution_count":127,"outputs":[{"execution_count":127,"output_type":"execute_result","data":{"text/plain":"member_id                     0\nloan_status                   0\nloan_amnt                     0\nissue_d                       0\nterm                          0\nint_rate                      0\ninstallment                   0\ngrade                         0\nemp_length                    0\nhome_ownership                0\nFICO                          0\nannual_inc                    0\nloantoincome                  0\ndti                           0\ndelinq_2yrs                   0\ninq_last_6mths                0\nopen_acc                      0\npub_rec                       0\nrevol_bal                     0\nrevol_util                    0\ntotal_acc                     0\ntot_cur_bal                   0\ntotal_rev_hi_lim              0\nacc_open_past_24mths          0\navg_cur_bal                   0\nbc_open_to_buy                0\nbc_util                       0\nmo_sin_old_il_acct            0\nmo_sin_old_rev_tl_op          0\nmo_sin_rcnt_rev_tl_op         0\nmo_sin_rcnt_tl                0\nmort_acc                      0\nmths_since_recent_bc          0\nnum_accts_ever_120_pd         0\nnum_actv_bc_tl                0\nnum_actv_rev_tl               0\nnum_bc_sats                   0\nnum_bc_tl                     0\nnum_il_tl                     0\nnum_op_rev_tl                 0\nnum_rev_accts                 0\nnum_rev_tl_bal_gt_0           0\nnum_sats                      0\nnum_tl_op_past_12m            0\npct_tl_nvr_dlq                0\npercent_bc_gt_75              0\ntot_hi_cred_lim               0\ntotal_bal_ex_mort             0\ntotal_bc_limit                0\ntotal_il_high_credit_limit    0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## 数据预处理-多维描述1.3","metadata":{}},{"cell_type":"code","source":"## 统计 FICO\ndef plot_fico(columns):\n    fico = ['mean', 'std', 'median']\n    rows = len(fico)\n    cols = len(columns)\n    fig = plt.figure(figsize=(16,4))\n    for row in range(cols):\n        for col in range(rows):\n            ax1 = fig.add_subplot(cols,rows,row*rows+col+1)\n            ax1.set_title('{}_{}'.format(columns[row],fico[col]))\n            if col == 0:\n                ax1 = df.groupby('grade')[columns[row]].mean().plot(kind='bar')\n            if col == 1:\n                ax1 = df.groupby('grade')[columns[row]].std().plot(kind='bar')\n            if col == 2:\n                ax1 = df.groupby('grade')[columns[row]].median().plot(kind='bar')\n# plot_fico(['int_rate', 'loan_status', 'emp_length', 'home_ownership'])\nplot_fico(['int_rate'])","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:21:25.109339Z","iopub.execute_input":"2022-10-23T07:21:25.110521Z","iopub.status.idle":"2022-10-23T07:21:25.839508Z","shell.execute_reply.started":"2022-10-23T07:21:25.110480Z","shell.execute_reply":"2022-10-23T07:21:25.838216Z"},"trusted":true},"execution_count":128,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x288 with 3 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"## 类别特征统计频数\nfig = plt.figure(figsize=(16,32))\n# ax1 = fig.add_subplot(3, 1, 1)\nax1.set_title('loan_status')\nax1 = df.groupby('grade')['loan_status'].value_counts().unstack().plot(kind='bar')\n# ax2 = fig.add_subplot(3, 1, 2)\nax2.set_title('emp_length')\nax2 = df.groupby('grade')['emp_length'].value_counts().unstack().plot(kind='bar')\n# ax3 = fig.add_subplot(3, 1, 3)\nax3.set_title('home_ownership')\nax3 = df.groupby('grade')['home_ownership'].value_counts().unstack().plot(kind='bar')","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:34:33.932257Z","iopub.execute_input":"2022-10-23T07:34:33.932951Z","iopub.status.idle":"2022-10-23T07:34:35.023599Z","shell.execute_reply.started":"2022-10-23T07:34:33.932915Z","shell.execute_reply":"2022-10-23T07:34:35.022464Z"},"trusted":true},"execution_count":139,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x2304 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## 数据预处理-1.4数据变换","metadata":{}},{"cell_type":"code","source":"grade_vc = df['grade'].value_counts()\ngrade_ls_vc = df.groupby('grade')['loan_status'].value_counts()\ngrade_ls_vc = pd.DataFrame(grade_ls_vc)\ngrade_ls_vc.columns = ['loan_status_n']\n\n# 求好坏比率\ngrade_level = pd.Series(grade_vc.index.tolist())\ng_b = pd.DataFrame(index = grade_level,columns=list(['g_b','good','bad']))\nfor i in grade_level:\n    grade_1 = grade_ls_vc.loc[(i,slice(None)),'loan_status_n']\n    g_b.loc[i,'g_b'] = grade_1[0]/grade_1[1]\n    g_b.loc[i,'good'] = grade_1[0]\n    g_b.loc[i,'bad'] = grade_1[1]\n# 绘制各分类柱状图\nplt.figure(figsize=(13, 8))\ng_b['g_b'].plot.bar()# one variable\nfor i, v in enumerate(g_b['g_b']):\n    plt.text(i, v+0.1, '%.2f' % v, color='blue', fontweight='bold',ha='center', va= 'bottom',fontsize=10)","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:26:24.515021Z","iopub.execute_input":"2022-10-23T07:26:24.515539Z","iopub.status.idle":"2022-10-23T07:26:24.789012Z","shell.execute_reply.started":"2022-10-23T07:26:24.515451Z","shell.execute_reply":"2022-10-23T07:26:24.788140Z"},"trusted":true},"execution_count":132,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 936x576 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# 粗分类后。（A-C分为一类）（D-G分为一类）\ng_b.loc['A-C'] = g_b.loc[['A','B', 'C'],:].apply(lambda x: x.sum())\ng_b.loc['D-G'] = g_b.loc[['D','E','F','G'],:].apply(lambda x: x.sum())\ng_b['g_b1'] = g_b['good']/g_b['bad']\ng_b = g_b.drop(['A','B','C','D','E','F','G'])\n\nplt.figure(figsize=(13, 8))\ng_b['g_b'].plot.bar()# one variable\nplt.title('after')\nfor i, v in enumerate(g_b['g_b']):\n    plt.text(i, v+0.1, '%.2f' % v, color='blue', fontweight='bold',ha='center', va= 'bottom',fontsize=10)","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:26:43.462107Z","iopub.execute_input":"2022-10-23T07:26:43.463471Z","iopub.status.idle":"2022-10-23T07:26:43.683344Z","shell.execute_reply.started":"2022-10-23T07:26:43.463418Z","shell.execute_reply":"2022-10-23T07:26:43.682464Z"},"trusted":true},"execution_count":133,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 936x576 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## 数据预处理-1.5","metadata":{}},{"cell_type":"code","source":"\n# home_ownership各类别间没有比较的关系，转换成哑变量，避免比较关系\nho_ = pd.get_dummies(df['home_ownership'])\npd.merge(df, ho_, on='id')\nho_","metadata":{"execution":{"iopub.status.busy":"2022-10-23T06:55:33.338322Z","iopub.execute_input":"2022-10-23T06:55:33.338766Z","iopub.status.idle":"2022-10-23T06:55:33.397344Z","shell.execute_reply.started":"2022-10-23T06:55:33.338728Z","shell.execute_reply":"2022-10-23T06:55:33.396093Z"},"trusted":true},"execution_count":94,"outputs":[{"execution_count":94,"output_type":"execute_result","data":{"text/plain":"          MORTGAGE  OWN  RENT\nid                           \n11435650         1    0     0\n11385707         0    0     1\n11405625         0    0     1\n11375637         0    0     1\n11405664         0    1     0\n...            ...  ...   ...\n20798851         1    0     0\n20778925         1    0     0\n20739103         0    1     0\n20718887         0    0     1\n20778858         1    0     0\n\n[19831 rows x 3 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MORTGAGE</th>\n      <th>OWN</th>\n      <th>RENT</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>11435650</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11385707</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11405625</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11375637</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11405664</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>20798851</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20778925</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20739103</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20718887</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>20778858</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>19831 rows × 3 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# grade转换成递增的数字，因为数字和grade都隐含比较关系\ndef mapfunc(x):\n    if x == 'A':\n        return 1\n    if x == 'B':\n        return 2\n    if x == 'C':\n        return 3\n    if x == 'D':\n        return 4\n    if x == 'E':\n        return 5\n    if x == 'F':\n        return 6\n    if x == 'G':\n        return 7\ndf['grade'].map(mapfunc)","metadata":{"execution":{"iopub.status.busy":"2022-10-23T06:57:49.410911Z","iopub.execute_input":"2022-10-23T06:57:49.412035Z","iopub.status.idle":"2022-10-23T06:57:49.431575Z","shell.execute_reply.started":"2022-10-23T06:57:49.411980Z","shell.execute_reply":"2022-10-23T06:57:49.430495Z"},"trusted":true},"execution_count":96,"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"id\n11435650    2\n11385707    4\n11405625    3\n11375637    2\n11405664    1\n           ..\n20798851    1\n20778925    3\n20739103    2\n20718887    5\n20778858    1\nName: grade, Length: 19831, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## 数据可视化-2.1","metadata":{}},{"cell_type":"code","source":"# int_rate箱形图\nsns.boxplot(x=df['int_rate'], palette=\"Blues\", width=0.3,linewidth=3)#Horizontal","metadata":{"execution":{"iopub.status.busy":"2022-10-23T06:59:48.210930Z","iopub.execute_input":"2022-10-23T06:59:48.211791Z","iopub.status.idle":"2022-10-23T06:59:48.413549Z","shell.execute_reply.started":"2022-10-23T06:59:48.211743Z","shell.execute_reply":"2022-10-23T06:59:48.412172Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='int_rate'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# int_rate概率密度图\ndf['int_rate'].plot.kde()","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:01:15.555983Z","iopub.execute_input":"2022-10-23T07:01:15.556593Z","iopub.status.idle":"2022-10-23T07:01:16.209140Z","shell.execute_reply.started":"2022-10-23T07:01:15.556542Z","shell.execute_reply":"2022-10-23T07:01:16.207983Z"},"trusted":true},"execution_count":100,"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:ylabel='Density'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAX4AAAD4CAYAAADrRI2NAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAnlUlEQVR4nO3deXxcV3338c9Po32xrN22LFleY+zYwbGSOCsJCQGakARISyAJgUBSCk+aQkufULoApZQCpQQoS9iSFMoOIU8IkH11YrBjJ/EuebdkS7Jk7bvmPH/MyJFtyR5JM3Pnznzfr5deGs3cuffn0czXR+eee4455xARkdSR5nUBIiISXwp+EZEUo+AXEUkxCn4RkRSj4BcRSTHpXhcQidLSUldTU+N1GSIivrJhw4YjzrmyE+/3RfDX1NSwfv16r8sQEfEVM9s33v3q6hERSTEKfhGRFKPgFxFJMQp+EZEUo+AXEUkxCn4RkRSj4BcRSTEKfklaXf1DfPfZ3TxXd8TrUkQSii8u4BKZrGDQ8Zf/s4G1u1oB+NKfn8X1q+d6XJVIYlCLX5LSo9uaWLurlX++ehnnzS/mUw9u4WjPoNdliSQEBb8kpR+t28/swmzee/48/vW6M+keGOYHz+/xuiyRhKDgl6TT2T/E2vojXLeqkvRAGksqCnjL8ln8YO1eegaGvS5PxHMKfkk6z+48wnDQ8cal5cfuu+2S+XT1D/OrjQ0eViaSGBT8knT+uKeV3MwAq6pmHrvv7OoiVlQWcu/ze3DOeVecSAKIWfCb2ffNrNnMNo+574tmtt3MXjGzX5vZzFgdX1LXxgPtnDV3JumB197eZsb7L6xhV0sPz2p4p6S4WLb47wXecsJ9jwJnOudWAjuBT8Tw+JKC+odG2NrYyarqmSc9dtXK2ZTmZ3Hv2r1xr0skkcQs+J1zzwBtJ9z3iHNu9Ozai4AGVktUvdrQwXDQcXZ10UmPZaUHuPG8ap7Y3syeIz0eVCeSGLzs478V+N1ED5rZ7Wa23szWt7S0xLEs8bPNDR0ArJxbOO7jN66pJiNg3KdWv6QwT4LfzD4JDAM/mmgb59w9zrla51xtWdlJS0aKjGtnUxczczMoK8ga9/HygmyuXjmHX2w4SFf/UJyrE0kMcQ9+M3sfcDVwo9PwComynU3dLKkowMwm3ObWC+fTPTDMj9btj2NlIokjrsFvZm8B/h64xjnXG89jS/JzzrGzqYslFfmn3G7F3ELesKSMe57ZrQu6JCXFcjjnj4EXgDPM7KCZfQD4OlAAPGpmm8zsW7E6vqSew539dPUPs6Si4LTb3nnFYtp6Bvnhi/viUJlIYonZ7JzOuXePc/f3YnU8kZ1N3QARBf/Z1UVcEm7133z+PHIzNVGtpA5duStJo66pC4gs+AHuvHwxrWr1SwpS8EvSqG/upiQvk+K8zIi2Xz2viIsXl/Ltp3czMDwS4+pEEoeCX5LG/rZe5pXkTuo5t140n9aeQZ7aoWtFJHUo+CVp7G/rpbp4csF/8aJSSvIy+c0mzdopqUPBL0lhaCRIY3vfpIM/PZDGVStn8/i2ZvqH1N0jqUHBL0mhsb2PoIOqSQY/wGVLyxkYDvLHPW2n31gkCSj4JSnsbwtdDzjZFj/AmvklZKan8fRO9fNLalDwS1I4FvyTPLkLkJMZ4Lz5xTxbp+CX1KDgl6Swv62XzEAaFQXZU3r+mgUl7Gzqpr13MMqViSQeBb8khQNtvcwtziEtbeLJ2U5ldP7+jfvbo1iVSGJS8EtSmMpQzrHOqiokkGas36cTvJL8FPySFPa3Ti/4czPTWT5nBhv2HY1iVSKJScEvvtfRO0Rn//C0gh9gRWUhWxo70TIRkuwU/OJ7oyN6pjKGf6zlcwrp6h/mQFtfNMoSSVgKfvG96YzhH2v5nBkAbGnsmHZNIolMwS++F60W/xmzCgikGZsV/JLkFPzie/vbeinJyyQ/a3qLqWRnBFhcns+Wxs4oVSaSmBT84nsH2nqn3doftWzODAW/JD0Fv/jedMfwj7Vs9gxaugZo7R6Iyv5EEpGCX3xteCRIwxSmY57I6LKNo+v3iiQjBb/42qGOfkaCLurBX9fcFZX9iSQiBb/4WrRG9IyqmJFFQXY6Ow4r+CV5xSz4zez7ZtZsZpvH3FdsZo+aWV34e1Gsji+pYTrTMY/HzDijooA6dfVIEotli/9e4C0n3HcX8LhzbjHwePhnkSnb39ZLRsCYNWNq0zGPZ3FFATuaujR1gyStmAW/c+4Z4MSpDq8F7gvfvg+4LlbHl9Swv62XuUW5BKY4HfN4zqjIp6NviJYujeyR5BTvPv4K59yh8O3DQEWcjy9JJppj+EdpZI8kO89O7rrQ39ET/i1tZreb2XozW9/SoiXxZHz7WnupLs6J6j6XzAoF/44mneCV5BTv4G8ys9kA4e/NE23onLvHOVfrnKstKyuLW4HiHx29Q3T0DTGvOC+q+y3Nz6I4L5M6Bb8kqXgH/4PALeHbtwC/ifPxJYnsa+sBojeiZ6zF5flq8UvSiuVwzh8DLwBnmNlBM/sA8HngTWZWB1wR/llkSva1hoZyzotB8J8xKzSkUyN7JBlNbzrDU3DOvXuChy6P1TEltexrDbf4o3xyF0JDOrsHhmns6KdyZnTPIYh4TVfuim/ta+2lvCCL3Mzot18Wl+cDUN+skT2SfBT84lv72npj0s0DsEjBL0lMwS++tb+1l+ooj+gZVZKXSVFuBvWarE2SkIJffKl/aITDnf0xa/GbGYvK89Xil6Sk4BdfGp2cLVbBD7CovIC6Zo3skeSj4Bdfem0oZ2y6eiDUz9/eO0Rrz2DMjiHiBQW/+NLoUM55MRjKOUojeyRZKfjFl3a1dFOUm0FRXmbMjjE6sqdOwS9JRsEvvlTX1M3i8oKYHmN2YTZ5mQF2KfglySj4xXecc9S3dLMw3CKPldGRPVp/V5KNgl98p7VnkPbeoWNdMbG0UEM6JQkp+MV3RoN4cRyCf3F5AU2dA3T2D8X8WCLxouAX3xk92RqPFr+mbpBkpOAX39nV3E1eZoDZhdFbYH0iCn5JRgp+8Z0dh7tYVFGAWfQWWJ9IVVEOmelpCn5JKgp+8RXnHFsaO1g2e0ZcjpceSGNBaZ6CX5KKgl98paG9j87+YZbPiU/wAxrSKUlHwS++sqWxEyDuwX/waB/9QyNxO6ZILCn4xVe2NnaSZrB0VvyCf3F5Ac6FpokQSQYKfvGVLY2dLCjLJyczELdjamSPJBsFv/jK1jie2B1VU5pLmin4JXko+MU3jvYM0tjRH9f+fYCs9AA1JXnUNSn4JTko+MU3th4KndhdFufgB1hQls/uIwp+SQ6eBL+ZfdTMtpjZZjP7sZnF/hJM8b0tjR0ALJ9TGPdj15Tksr+tl2BQyzCK/8U9+M2sEvhroNY5dyYQAG6Idx3iP1saO5lTmE1xDBdfmci80jz6h4I0dw3E/dgi0eZVV086kGNm6UAu0OhRHeIjmxs6WOZBax9CLX6AveElH0X8LO7B75xrAL4E7AcOAR3OuUdO3M7Mbjez9Wa2vqWlJd5lSoLpHRxm95GeuJ/YHVUTXtR97xEFv/ifF109RcC1wHxgDpBnZjeduJ1z7h7nXK1zrrasrCzeZUqC2XaoC+fie8XuWLMLs8kIGHtbez05vkg0edHVcwWwxznX4pwbAn4FXOBBHeIjW0dP7FZ609WTHkijqiiXferqkSTgRfDvB9aYWa6F5tW9HNjmQR3iI1saOynKzWBOHObgn8i8kly1+CUpeNHHvw74BfAS8Gq4hnviXYf4y5bGTpbPKYzLHPwTmVeSx77WHpzTkE7xN09G9Tjn/sU5t9Q5d6Zz7mbnnMbIyYSGRoLsONzlWf/+qJqSXHoHR2jp1ttV/E1X7krC29fay+BIkCUVBZ7WUVM6OrJH3T3ibwp+SXijk6Mtroj94uqnUl0cGst/8KiCX/xNwS8Jb3Qe/IVl3gb/nJk5ABxo6/O0DpHpUvBLwqtr6mJOYTZ5Weme1pGdEaC8IEstfvE9Bb8kvPqWbhaWe9vaH1VVnMvBo2rxi79FFPxm9iszu8rM9B+FxFUw6NjV3HNsFSyvzS3K4WC7Wvzib5EG+TeA9wB1ZvZ5MzsjhjWJHNPY0Uff0EhCBX9jez/DI0GvSxGZsoiC3zn3mHPuRuBsYC/wmJmtNbP3m1lGLAuU5DE8EuSVg+109Q9F/JxjI3rKvR3KOWpuUS4jQcfhzn6vSxGZsoi7bsysBHgf8EFgI3A3of8IHo1JZeIrI0FH/9DIhI/3D43wjm+u5ZqvP88b//Np6pu7ItrvaPAnSou/qmh0SKf6+cW/Iu3j/zXwLKG589/mnLvGOfdT59wdQGJ8IsUzR3sGefNXnmHlpx/ht68cGnebrzxWxysHO/joFUsIBh1/+7OXI5r6oL65m+K8TE8WXxnP3KLQkE4Fv/hZpC3+7zjnljnn/t05dwjAzLIAnHO1MatOfOF7z+1hd0s31cW5/O3PN3Gg7fiTn68e7OA7z+7mXbVV3HnFYv7vW5by8sEOnqk7ctp91zd3s8jj8ftjzZ6ZjRkn/RtF/CTS4P/sOPe9EM1CxJ+CQcfP1h/g0jPKuf/WczGMf31o67HHB4eDfPwXL1Oan8k/XPU6AK5bVUlpfhb/u27fKfftnEuooZwAWekBKgqy1eIXXztl8JvZLDNbTWiZxFVmdnb461JC3T6S4rYf7qK5a4CrVsxmzswc7rh8EY9sbeLJ7c0AfPOpXWw/3MVnr1tBYU5oHEBmehpXrZjFUzta6BkYnnDfrT2DtPcOsTiBgh+gqjhHF3GJr52uxf9mQsskzgW+DPxn+OtjwD/EtjTxg3V7WgE4b0ExAB+8aAELy/L4+1++wlcfr+Pux3dy7evn8KZlFcc9760rZjMwHOSJ8H8Q46lrSqwTu6PmFukiLvG3Uwa/c+4+59xlwPucc5eN+brGOferONUoCeyl/e3MKcxmbni0S2Z6Gt+6aTUAX350J6uqi/jsdWee9Lxzaoopys3gyR0TB399S2JMznaiuUU5HOroY0hj+cWnTjn5iZnd5Jz7IVBjZh878XHn3JdjVpn4ws7DXSydffw8+YsrCnj645eyv62XxeUFBNJOXjwlkGZcsLCUtfWtOOfGXWClvqmL/Kx0Zs3wbtWt8cwtyiHo4HBHP1XF6vEU/zldV09e+Hs+UDDOl6SwoZEgu490jztPfm5mOktnzRg39EddsKiEw5397D4y/jq2dc2hE7terro1ntGx/AfUzy8+dcoWv3Pu2+Hvn45POeIn+1p7GBpxLJliV8xFi0oBWFt/ZNwpl+ubu7lkSdm0aoyF0W6tg219sNDjYkSmINILuL5gZjPMLMPMHjezFjO7KdbFSWLbcTjUBz/VlbGqi3OpnJnD8/WtJz3W0TtEc9dAwp3YhdBY/jTTgiziX5GO47/SOdcJXE1orp5FwMdjVZT4Q11zF2ZTH3VjZly4qIQXdrcyEjz+Kt76ltCUDok2lBMgI5DG7MIcjewR34o0+Ee7hK4Cfu6c64hRPeIj+9t6mT0jm+yMwJT3ceGiUjr6hth2qPO4+3c2JdbkbCeqLMphv67eFZ+KNPgfMrPtwGrgcTMrAzQ9YYo7eLSPyvDcNVN1/oISAJ6vP376hlcbOijITqeqeHr7j5Xq4lyd3BXfinRa5ruAC4Ba59wQ0ANcO9WDmtlMM/uFmW03s21mdv5U9yXeaTjad+xE51SVz8hmcXk+z+86vp//1YMdrJxbmHAjekbNK86lqXPglDOSiiSqyayotRR4l5m9F7geuHIax70b+L1zbilwFrBtGvsSDwyPBDnc2U/lzOm3yC9cVMqf9rQxOBy6IGpgeITthztZUTlz2vuOleqS8JBOdfeID0U6qud/CE3dcBFwTvhrSrNymlkhcAnwPQDn3KBzrn0q+xLvHO7sZyTojk1TPB3nLyyhb2iETQfaAdhxuIuhEcfKuYXT3nesVIcv3NrXquAX/znlOP4xaoFlLpIJ1E9vPtAC/MDMzgI2AHc658a/ikcSUkN4RMt0+/gB1iwoIc3g2boWzp1fzNpwt8/Z1UXT3nesjAa/TvCKH0Xa1bMZmBWlY6YTWrnrm865VYTOF9x14kZmdruZrTez9S0tLVE6tETL6FDGaHT1FOZkcN78Eh565RDOOZ7a0czSWQXMKkysqRrGKs7LJD8rXcEvvhRp8JcCW83sD2b24OjXFI95EDjonFsX/vkXhP4jOI5z7h7nXK1zrrasLPGu3kx1De2h4J8TheAHePuqSvYc6eH+F/axbk8bVy6PVjsjNsyMquJcBb/4UqRdPZ+K1gGdc4fN7ICZneGc2wFcDmw93fMksRw82ktZQda0xvCPde2qOXztyTr+5cEtZKWncdOa6qjsN5bmFecem0FUxE8iCn7n3NNmNg9Y7Jx7zMxygel84u8AfmRmmcBu4P3T2Jd4oKG9LyondkdlpQf49k213PPMLq59fSXlBYnbzTOquiSXJ3c0Eww60k4xGZ1Iooko+M3sNuB2oJjQtFSVwLcItdYnzTm3iSmOCpLE0HC0jzMrozvqZtmcGXzlhlVR3WcsVRfnMjAcpLlrIKHPR4icKNI+/o8AFwKdAM65OqA8VkVJYgsGHY3t/VEZ0eNnGtkjfhVp8A845wZHfzCzdCAaQzvFh1q6BxgcCU77ql2/e20sv0Yii79EGvxPm9k/EFp0/U3Az4H/F7uyJJGNTkc8N0ojevyqsiiHNFOLX/wn0uC/i9BFV68Cfwk8DPxjrIqSxDY6hj+aJ3f9KCOQRnVxLrtb1OIXf4l0VE/QzB4AHnDO6WqqFHcwilft+t3iigJ2NnV5XYbIpJyyxW8hnzKzI8AOYEd49a1/jk95koga2vsozsskNzPSy0CS1+LyfPYc6WFoJOh1KSIRO11Xz0cJjeY5xzlX7JwrBs4DLjSzj8a8OklIDUf7ojJVQzJYUlHAcNCxd4IF40US0emC/2bg3c65PaN3OOd2AzcB741lYZK4Dh7tVfCHjS47WdesK3jFP04X/BnOuSMn3hnu58+ITUmSyJxzNLRPf+WtZLGoPB8z1M8vvnK64B+c4mOSpFq6B+gfCh4bw57qsjMCVBfnqsUvvnK6s3NnmVnnOPcboGvUU9CBttCInkRdC9cLZ1QUnLRYvEgiO2XwO+eiM/WiJI3Ri7fU4n/NyrmFPLK1ic7+IWZkqwdUEt9k1twVYX94qcFUn65hrJVzZwKw+WCHt4WIREjBL5NyIMrz8CeD0bWBNx1s97YQkQgp+GVS9rf1qpvnBDNzM6kpyWXT/navSxGJiIJfJuVAWx9VGsp5knPnF7NuTxsjQU1aK4lPwS8RGxoJcqijjyq1+E9ywcJSOvqG2Nqo0T2S+BT8ErHG9j6CDgX/OC5YWALA87tOut5RJOEo+CVio9MPzy/N87iSxFM+I5ulswp4bGuT16WInJaCXyI2Oi3BkvICjytJTFetmM36fUc51NHndSkip6Tgl4jtbOqmvCCLwlxdpDSeq1bOBuChlw95XInIqSn4JWJ1zV0sqVBrfyILyvJZPa+I+1/cq9E9ktAU/BKRYNBR39x9bBpiGd8HL5rPgbY+frdZrX5JXJ4Fv5kFzGyjmT3kVQ0SuQNHe+kdHOGMWWrxn8qVy2expCKff394O32DI16XIzIuL1v8dwLbPDy+TMKmA+0AnBWel0bGF0gzPnPtmTS09/GJX72Cc+rykcTjSfCb2VzgKuC7XhxfJm/j/nZyMgIsqVBXz+msWVDC3125hAc2NfLhH71EU2e/1yWJHMer1bK/Avw9MGG/gZndDtwOUF1dHZ+qZELr9rTx+qqZpAd0WigSH7lsEZnpaXzh9zt4fHsz7zy7kvdfOF8nxyUhxP1TbGZXA83OuQ2n2s45d49zrtY5V1tWVhan6mQ8zZ39bDvUycVLSr0uxTfMjNsvWciTf3cp16+ey69eauDK/3qGzz60leGRoNflSYrzovl2IXCNme0FfgK80cx+6EEdEqHHtjUDcOmSco8r8Z+q4lw+9/YVvPCJy7lpTTXffW4PH/+F+v7FW3EPfufcJ5xzc51zNcANwBPOuZviXYdExjnHzzccYHF5Pq+brW6KqSrOy+Sz163gY29awq83NnDv2r1elyQpTB22ckqPb2tm4/52blozDzPzuhzfu+ONi3jj0nL+/Xfbjy1jKRJvnga/c+4p59zVXtYgExsYHuGzv93KwrI83nOeTrBHg5nxr9ediQFf/MMOr8uRFKUWv0zoB8/vZW9rL//8tuVkaDRP1FTOzOG2ixfwm02NbGnUOr0Sf/o0y7jaegb5+hP1XPG6ct6wRKOqou22SxaQn5XOt57e7XUpkoIU/DKu7zy7m57BYe5661KvS0lKhTkZ3Limmt++0si+1h6vy5EUo+CXk/QODnP/2r1cvXIOizT3fsx84ML5pKelcc8zavVLfCn45SSPb2umZ3CEG3VCN6bKZ2TzztWV/HzDQVq6BrwuR1KIgl9O8uDLjVTMyOKcmmKvS0l6H7x4AUMjQe5/Ya/XpUgKUfDLcQaGR3i2roW3njmbQJrG7cfawrJ8rlxWwf0v7KNnYNjrciRFKPjlOC8f6KB/KMgFC0u8LiVl/OUbFtLRN8RP/3TA61IkRSj45Tgv7GrFDM6br+CPl7Orizi3ppjvPbeHIU3gJnGg4JfjvLi7lWWzZ2hB9Tj70KULaGjv44GNDV6XIilAwS/HBIOOVxs6OLu6yOtSUs6lS8pZObeQLz2yQ339EnMKfjlmb2sP3QPDrKgs9LqUlJOWZvzL25bR1DnA15+s97ocSXIKfjlmc2MnAMsrZ3hcSWpaPa+YP189l28/vYsXdrV6XY4kMQW/HLO5oYPMQJqWB/TQp65ZzrySPO748Ub2HtFUDhIbCn45ZmtjJ2fMKtBMnB7Ky0rnO+9dzUgwyI3fXcehjj6vS5IkpE+4HFPX3KXWfgJYVF7AfbeeS0ffEDd+Zx3NXf1elyRJRsEvAHT2D9HUOcCi8nyvSxFg5dyZ3Pv+czjc2c+N31lHa7fm8pHoUfALAPXN3QAsVvAnjNqaYr57Sy3723r50A836OIuiRoFvwBQ3xQKfrX4E8sFC0v5wvUr+dPeo3zu4W1elyNJQsEvANS3dJOZnkZVca7XpcgJrn19Je+7oIYfPL+XZ3a2eF2OJAEFvwBQ19TFgtI8zciZoO5661IWlOXxD79+VVf2yrQp+AUItfjVzZO4sjMCfOGdK2lo7+Mrj+30uhzxubgHv5lVmdmTZrbVzLaY2Z3xrkGO1zc4wsGjfSzWMosJrbammHfVVnHv2r3s0cVdMg1etPiHgb91zi0D1gAfMbNlHtQhYbtaunFOJ3b94GNXLiEzkMbnf6cTvTJ1cQ9+59wh59xL4dtdwDagMt51yGt2tYSHclYo+BNdeUE2H75sEX/Y0sSLuzWfj0yNp338ZlYDrALWjfPY7Wa23szWt7RoJEMs1TV1E0gzakryvC5FIvCBi+ZTMSOLLz+yE+ec1+WID3kW/GaWD/wS+BvnXOeJjzvn7nHO1TrnasvKyuJfYArZ1dLNvOJcMtN1rt8PsjMCfPjSRfxxbxtrNYunTIEnn3QzyyAU+j9yzv3KixrkNbtaullQpm4eP3nXOVXMmpHNfz2qVr9Mnhejegz4HrDNOffleB9fjjc8EmTPkR6d2PWZ7IwAH7lsIev3HeW5+iNelyM+40WL/0LgZuCNZrYp/PVnHtQhwIGjfQyNOBaWqX/fb/7inCpmF2Zz92N1avXLpHgxquc555w551Y6514f/no43nVIyK5mzdHjV1npAf7q0lCrXyt2yWTobF6Kqw8P5VQfvz/9RW0VFTOyuPvxOq9LER9R8Ke4Xc3dlBVkUZiT4XUpMgXZGQE+9IaFrNvTpnH9EjEFf4rb1dLNIrX2fe3d51ZTmp/F155Qq18io+BPYc456pu7WViuE7t+Fmr1L+D5+lbW723zuhzxAQV/CjvSPUhn/zAL1eL3vfecV01JXiZffaLe61LEBxT8KWx0jh6N6PG/3Mx0brtkAc/sbGHj/qNelyMJTsGfwuqaugDU4k8SN6+ZR1FuBl9Tq19OQ8GfwrYe6mRmbgazC7O9LkWiIC8rnQ9evIAntjdrXL+ckoI/hW1t7GTZ7BmEZtGQZHDrhfOpKs7hk79+lf6hEa/LkQSl4E9RwyNBth/uYtnsGV6XIlGUkxng365bwe4jPXzlMQ3vlPEp+FPUniM9DAwHWTZHwZ9sLllSxg3nVPGtp3fx21cOeV2OJKB0rwsQb2w9FFoCQcGfnD597XJ2NHXx0Z9uYmB4hLevqlSXnhyj4E9RWxs7yQykaURPkspKD3Dv+87ltvvX87Gfvcx9L+xjVdVMuvqH2d/Ww54jvXQPDLGgNJ+3r6rk5vPnkZ0R8LpsiRN19aSol/Yf5XVzZpAR0FsgWRXmZvC/t53HZ65dzkgwyC83HGTtrtDc/ZcvLefmNfPIzQzwbw9v47IvPcXvNx/S9M4pQi3+FNQ/NMLLBzq45YJ5XpciMZYeSOO959fw3vNrJtzmxd2tfOrBLXzohy9x2RllfObaM6kqzo1fkRJ3Cv4UtLmhg8GRILU1xV6XIglgzYISHrrjIu5du5f/enQnl3/5aS5aVMrCsjyGg46WrgGaOwc43NlPU2c/ZlCSl8Wq6pm84+xKLl1STlqazh/4iYI/Ba3bE5rIq3ZekceVSKJID6TxwYsXcNXK2Xz76d08vbOF5+uPkJ5mlBVkUT4jm9dXzaRiRhZmxqGOfl7YdYSHXjnEquqZ/Mc7V7KkosDrf4ZESMGfgp7a0cyy2TMoyc/yuhRJMLMLc/jUNcsj2nZwOMgDmxr4/O+2c83Xn+OL15/F286aE+MKJRp0Zi/FHO0ZZMO+o1zxunKvSxGfy0xP4y9qq/j931zM8jmF3PHjjXzh99sJBnWCONEp+FPMY9uaCDq4/HUVXpciSaK8IJsf37aGd59bxTee2sXt/7Oe7oFhr8uSU1Dwp5if/OkA80vzWFFZ6HUpkkQy09P43NtX8OlrlvPkjhbe8Y3n2Rme/VUSj4I/hWxu6GDDvqPctGaeRmFI1JkZt1xQw/23nsuR7kGu+uqz/OcjO+jsH/K6NDmBgj9FOOf4j99vpzAng+tXz/W6HEliFy4q5dGPXsKfrZjN156o56LPP8EX/7CdA229XpcmYZ4Ev5m9xcx2mFm9md3lRQ2p5htP7eLZuiN87E1LKMzJ8LocSXIl+VncfcMqHrrjItYsKOGbT+3iki8+yS3f/yMPbGzQXwEes3hfom1mAWAn8CbgIPAn4N3Oua0TPae2ttatX78+ThUmj/6hEbYd6uS+tXt5YFMj15w1h7tveL0m65K4a2zv46d/OsDP1h/gUEc/mYE0LlxUwrnzSzirqpCakjzKC7JI1xQiUWVmG5xztSfe78U4/nOBeufcbgAz+wlwLTBh8E/VVx+v48GXGwGOzUFy3H9zjpPuG/sfoTt239injHncnfz4cbsf55jj7ev4+8bbdpxjRlBzZ/8QzoVOvP31Gxdx5xVLFPriiTkzc/jom5Zw5+WL2XignYdfPcQT25t5ckfLsW0CaUZuZoDsjAA5GQEC4fNQx96xdty3Y+/lZH9Hf+4dKzgnylfZexH8lcCBMT8fBM47cSMzux24HaC6unpKByovyOKMsVcTnvDGCR/nxIcZm40nvslOfP5r+xzz+LjPH3vfydvaeDsd+/hxNUd2zKK8TBaV53PRolJm5mYi4rW0NGP1vCJWzyvin65eRlvPIJsbOjh4tI9DHX10DwzTPzRC3+AIQTe2IXNCI+pYAyj5rxnIicGsqQl75a5z7h7gHgh19UxlHzecW80N507tPw0Rib3ivEwuWVLmdRkpx4sOtQagaszPc8P3iYhIHHgR/H8CFpvZfDPLBG4AHvSgDhGRlBT3rh7n3LCZ/R/gD0AA+L5zbku86xARSVWe9PE75x4GHvbi2CIiqU6DZkVEUoyCX0QkxSj4RURSjIJfRCTFxH2unqkwsxZg3yk2KQWOxKmcyVBdk6O6Jkd1TU4q1jXPOXfSFXK+CP7TMbP1401E5DXVNTmqa3JU1+Sorteoq0dEJMUo+EVEUkyyBP89XhcwAdU1OaprclTX5KiusKTo4xcRkcglS4tfREQipOAXEUkxvgh+Mys2s0fNrC78vWiC7W4Jb1NnZreMuf+p8OLum8Jf5dOs55SLxZtZlpn9NPz4OjOrGfPYJ8L37zCzN0+njmjVZWY1ZtY35vX5VpzrusTMXjKzYTO7/oTHxv2dJkBdI2Ner6hOKx5BXR8zs61m9oqZPW5m88Y85uXrdaq6YvZ6RVjbh8zs1fDxnzOzZWMe8/IzOW5dsf5M4pxL+C/gC8Bd4dt3Af8xzjbFwO7w96Lw7aLwY08BtVGqJQDsAhYAmcDLwLITtvkw8K3w7RuAn4ZvLwtvnwXMD+8nkAB11QCbY/S7i6SuGmAlcD9wfSS/Uy/rCj/W7eHrdRmQG779V2N+j16/XuPWFcvXaxK1zRhz+xrg9+HbXn8mJ6orZp9J55w/WvyEFmO/L3z7PuC6cbZ5M/Coc67NOXcUeBR4SwxqObZYvHNuEBhdLH6ien8BXG5mFr7/J865AefcHqA+vD+v64ql09blnNvrnHsFCJ7w3Fj+TqdTVyxFUteTzrne8I8vElrFDrx/vSaqK9Yiqa1zzI95vLZ8r6efyVPUFVN+Cf4K59yh8O3DQMU424y3iHvlmJ9/EP6T6Z+mGXanO85x2zjnhoEOoCTC53pRF8B8M9toZk+b2cVRqinSumLx3FjvO9vM1pvZi2Z2XZRqmkpdHwB+N8XnxqsuiN3rFXFtZvYRM9tFqAfhryfzXA/qgth9JhNnsXUzewyYNc5Dnxz7g3POmdlk/1e80TnXYGYFwC+Bmwn9+S4hh4Bq51yrma0GHjCz5Se0RuR488LvqQXAE2b2qnNuVzwLMLObgFrgDfE87ulMUJfnr5dz7r+B/zaz9wD/CET1HMhUTVBXTD+TCdPid85d4Zw7c5yv3wBNZjYbIPy9eZxdTLiIu3Nu9HsX8L9M70+5SBaLP7aNmaUDhUBrhM+Ne13hP3NbAZxzGwj1Sy6JY12xeG5M9z3mPbWb0DmkVfGsy8yuINQousY5NzCZ53pQVyxfr4hrG+MnvNZd7PlrNl5dMf5M+ubk7hc5/uTuF8bZphjYQ+ikVlH4djGhv2pKw9tkEOrb/tA0akkndNJsPq+dsFl+wjYf4fiTqD8L317O8SeSdhO9E0nTqatstA5CJ6IagOJ41TVm23s5+eTuSb/TBKirCMgK3y4F6jjhpF2Mf4+rCAXB4kg+AwlQV8xer0nUtnjM7bcB68O3vf5MTlRXzD6TzjnfBH8J8Hj4DfPY6AtA6M/J747Z7lZCJ2fqgfeH78sDNgCvAFuAu6f7iwX+DNgZfpN/MnzfZwi1cgCygZ+H6/gjsGDMcz8Zft4O4K1Rfp2mVBfwzvBrswl4CXhbnOs6h1D/Zw+hv4y2nOp36nVdwAXAq+EP8qvAB+Jc12NAU/j3tQl4MEFer3HrivXrFWFtd495jz/JmAD2+DM5bl2x/kxqygYRkRSTMH38IiISHwp+EZEUo+AXEUkxCn4RkRSj4BcRSTEKfhGRFKPgFxFJMf8fm6XiCw22LSgAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# revol_util的概率密度图\ndf['revol_util'].plot.kde()","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:01:40.366647Z","iopub.execute_input":"2022-10-23T07:01:40.367068Z","iopub.status.idle":"2022-10-23T07:01:41.005982Z","shell.execute_reply.started":"2022-10-23T07:01:40.367037Z","shell.execute_reply":"2022-10-23T07:01:41.004634Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:ylabel='Density'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## 数据可视化-2.2","metadata":{}},{"cell_type":"code","source":"# 不同grade类别中的好人和坏人比例\ndf.groupby('grade')['loan_status'].value_counts().unstack().plot(kind='bar')","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:33:19.410442Z","iopub.execute_input":"2022-10-23T07:33:19.410876Z","iopub.status.idle":"2022-10-23T07:33:19.687955Z","shell.execute_reply.started":"2022-10-23T07:33:19.410843Z","shell.execute_reply":"2022-10-23T07:33:19.686846Z"},"trusted":true},"execution_count":135,"outputs":[{"execution_count":135,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='grade'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# 同一幅图FICO可以复用上面的代码 \n## 统计 FICO\ndef plot_fico(columns, target):\n    fico = ['mean', 'std', 'median']\n    rows = len(fico)\n    cols = len(columns)\n    fig = plt.figure(figsize=(16,4))\n    for row in range(cols):\n        for col in range(rows):\n            ax1 = fig.add_subplot(cols,rows,row*rows+col+1)\n            ax1.set_title('{}_{}'.format(columns[row],fico[col]))\n            if col == 0:\n                ax1 = df.groupby(target)[columns[row]].mean().plot(kind='bar')\n            if col == 1:\n                ax1 = df.groupby(target)[columns[row]].std().plot(kind='bar')\n            if col == 2:\n                ax1 = df.groupby(target)[columns[row]].median().plot(kind='bar')\n# plot_fico(['int_rate', 'loan_status', 'emp_length', 'home_ownership'])\n\n# 传入不同的参数可以得到不同列的FICO\nplot_fico(['annual_inc'], target='grade')","metadata":{"execution":{"iopub.status.busy":"2022-10-23T07:10:42.508880Z","iopub.execute_input":"2022-10-23T07:10:42.509300Z","iopub.status.idle":"2022-10-23T07:10:43.000700Z","shell.execute_reply.started":"2022-10-23T07:10:42.509270Z","shell.execute_reply":"2022-10-23T07:10:42.999379Z"},"trusted":true},"execution_count":107,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x288 with 3 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}